Fast JPEG Codec on the GPU - Nvidiadeveloper.download.nvidia.com/GTC/PDF/GTC2012/PresentationPDF/S... · JPEG Codecs: GPU vs. CPU Performance summary for the fastest JPEG codecs Accusoft

Fast JPEG Coding on the GPU

Fyodor Serzhenko, Fastvideo, Dubna, Russia

Victor Podlozhnyuk, NVIDIA, Santa Clara, CA

© Fastvideo, 2011

Key Points

We implemented the fastest JPEG codec

Many applications using JPEG can benefit from our codec

High Speed Imaging

Data Path for High Speed Camera (500 – 1000 fps)

Camera data rate from 600 MB/s to 2400 MB/s.

Problem: how to record 1 hour or more?

Possible Solutions

RAID, SSD, online compression on FPGA / DSP / CPU / GPU

The fastest solution: JPEG compression on GPU

Camera External cables PCI-E Frame grabber Host Storage

Why JPEG

Popular open compression standard

Good image quality at 10x-20x compression ratio

Moderate computational complexity

Main Stages of Baseline JPEG Algorithm

Source Image Upload RGB→YUV Transform Image split to blocks 8x8

2D DCT Quantization Zig-Zag

RLE + DPCM Huffman Bitstream Download

JPEG Codecs: GPU vs. CPU

Performance summary for the fastest JPEG codecs

(*) - as reported by manufacturer

JPEG Codec (Q=50%, CR=13) Encode, MB/s Decode, MB/s

Fastvideo FVJPEG + GTX 680 5200 4500

Fastvideo FVJPEG + GTX 580 3500 3500

Intel IPP-7.0 + Core i7 3770 680 850

Intel IPP-7.0 + Core i7 920 430 600

Vision Experts VXJPG 1.4 (*) 500 --

Accusoft PICTools Photo (*) 250 380

Best JPEG encoder IP Cores

Results as reported by manufacturer

JPEG IP Core Encode MB/s

Cast Inc. JPEG-E 750

Alma-Tech SVE-JPEG-E 500

Visengi JPEG Encoder 405

JPEG Encoding Rates for GPU & CPU

% 25% 50% 75% 100%

0

1000

2000

3000

4000

5000

6000

7000 GTX 680 + FVJPEG

GTX 580 + FVJPEG

GT 555M + FVJPEG

GT 240 + FVJPEG

Core i7 3770 + IPP-7

Core i7 920 + IPP-7

Quality level

JPEG compression throughput, MB/s

JPEG Encoding Time for GeForce 580

1

2

3

4

5

6

7

8

9

10Time for JPEG Compression Stages (ms)

100%

95%

75%

50%

25%

10%

Host-to-Device

DCT/Quant/Zig

RLE+DPCM

HuffmanDevice-to-Host

Quality level

JPEG Encoding Time for GeForce 680

1

2

3

4

5

6

7

8

9

10Time for JPEG Compression Stages (ms)

100%

95%

75%

50%

25%

10%

Host-to-Device

DCT/Quant/Zig

RLE+DPCM

Huffman

Device-to-Host

Quality level

DCT and Entropy Encoding (GeForce 580)

% 25% 50% 75% 100%

0

10

20

30

40

50

DCT

RLE+DPCM

Huffman

Quality level

Throughput for JPEG encoding stages (GB/s)

JPEG Decoding

No good parallel algorithm is known for Huffman decoding

Restart markers is a standard feature supported by all decoders

Fully parallel JPEG decoding is still possible

Currently supported restart intervals: 0, 1, 2, 4, 8, 16, 32

JPEG Decoding Rates for GPU & CPU

% 25% 50% 75% 100%

0

1000

2000

3000

4000

5000

6000

7000 GTX 680 + FVJPEG

GTX 580 + FVJPEG

GT 555M + FVJPEG

GT 240 + FVJPEG

Core i7 3770 + IPP-7

Core i7 920 + IPP-7

Quality level

JPEG decompression throughput, MB/s

JPEG Decoding Time for GeForce GTX 580

1

2

3

4

5

6

7

8

9Time for JPEG Decompression Stages (ms)

100%

95%

75%

50%

25%

10%

Host-to-Device

IDCT/Quant/Zig

RLE+DPCM

Huffman Device-to-Host

Quality level

JPEG Decoding Time for GeForce GTX 680

1

2

3

4

5

6

7

8

9Time for JPEG Decompression Stages (ms)

100%

95%

75%

50%

25%

10%

Host-to-DeviceIDCT/Quant/Zig

RLE+DPCM Huffman

Device-to-Host

Quality level

Getting More Speed-up

GPUs with PCI-Express 3.0 interface

Concurrent copy and execution

Multi-GPU computing

Applications to 3D rendering

• Modern 3D applications are working with increasingly high-

resolution data sets

• JPEG is a standard color map storage format

• Decoding JPEG on the CPU has major drawbacks

• CPU-based decoding can be unacceptably slow even with

partial GPU acceleration

• Transferring raw decoded image or intermediate decoding

results over PCI-Express is much more expensive

• JPEG decoding on the GPU is a perfect solution to both problems

Applications to JPEG Imaging for Web

• Server-side image scaling to fit client devices.

• Thumbnail generation for big image databases.

Problem: how to cope with 100’s of millions images per day?

Method outline

• Get images from the database and load them to Host

• Image Decompression → Resize → Compression

• Store final images to the database or send them to users

Conclusion

Fast image coding on the GPU is reality

Modern GPUs are capable of running many non-floating point

algorithms efficiently

Future Work

SDK for FVJPEG codec for Windows / Linux

Optimized JPEG, MJPEG, JPEG2000

Multi-GPU computing

Custom software design

Questions?

Fyodor Serzhenko [email protected]

Victor Podlozhnyuk [email protected]

More info at www.fastcompression.com

PCs & Laptop for testing

ASUS P6T Deluxe V2 LGA1366, X58, Core i7 920, 2.67 GHz, DDR-III 6 GB, GPU GeForce GTX 580 or GeForce GT 240

ASUS P8Z77-PRO, Z77, Core i7 3770, 3.4 GHz, DDR-III 8 GB, GPU GeForce GTX 680 (cc = 3.0, 1536 cores)

OS Windows-7, 64-bit, CUDA 4.1, driver 296.10

Laptop

ASUS N55S, Core i5 2430M, DDR III 6 GB

GeForce GT 555M (cc = 2.1, 144 cores)

OS Windows-7, 64-bit, CUDA 4.1, driver 296.10

Baseline JPEG parameters for test

8-bit grayscale images

Compression quality from 10% to 100%

Default static quantization and Huffman tables

Test image: 7216 x 5408, 8-bit, CR = 12.8

8-thread encode/decode option for CPU

Conclusion: These parameters define the same calculation

procedures for CPU & GPU.

Test image